There is a significant body of work looking at the ethical considerations of large language models (LLMs): critiquing tools to measure performance and harms; proposing toolkits to aid in ideation; discussing the risks to workers; considering legislation around privacy and security etc. As yet there is no work that integrates these resources into a single practical guide that focuses on LLMs; we attempt this ambitious goal. We introduce LLM Ethics Whitepaper, which we provide as an open and living resource for NLP practitioners, and those tasked with evaluating the ethical implications of others’ work. Our goal is to translate ethics literature into concrete recommendations for computer scientists. LLM Ethics Whitepaper distils a thorough literature review into clear Do’s and Don’ts, which we present also in this paper. We likewise identify useful toolkits to support ethical work. We refer the interested reader to the full LLM Ethics Whitepaper, which provides a succinct discussion of ethical considerations at each stage in a project lifecycle, as well as citations for the hundreds of papers from which we drew our recommendations. The present paper can be thought of as a pocket guide to conducting ethical research with LLMs.

This intermediate-level tutorial, titled "Gen-RecSys", merges both industrial and academic perspectives on recent advances in Generative AI for recommender systems (beyond LLMs). It aims to highlight the transformative role of generative models in modern recommender systems, which have significantly impacted the AI field-particularly with the rise of large language models (LLMs) like ChatGPT-and have contributed to a rapid convergence of the fields of search, data mining, and recommendation. By providing attendees with a modern perspective on GenAI applications in recommendation, the tutorial will emphasize how generative models can drive recommendation by unlocking and interacting with rich data representations, including behavioral, textual, and multi-modal data-knowledge highly transferable across many applications of interest to the WSDM community. Participants will learn about the categorization of generative models in recommender systems based on underlying data modalities: (i) ID-based collaborative models, (ii) text-driven models such as LLMs, and (iii) multi-modal models. Within each category, various deep generative model paradigms (e.g., AR, GAN, diffusion models) will be introduced, along with insights into their application areas. The tutorial will also cover evaluation aspects, including benchmarks, metrics, and assessments of social and ethical impacts and harms. This tutorial presents a condensed version of the industrial and academic work featured in the forthcoming book at FntIR 2024-25, titled "Recommendation with Generative Models [7]," and a shorter version prepared, and presented by the team, see GenRecSys-Survey [6].

This paper investigates how generative AI can potentially undermine the integrity of collective knowledge and the processes we rely on to acquire, assess, and trust information, posing a significant threat to our knowledge ecosystem and democratic discourse. Grounded in social and political philosophy, we introduce the concept of generative algorithmic epistemic injustice. We identify four key dimensions of this phenomenon: amplified and manipulative testimonial injustice, along with hermeneutical ignorance and access injustice. We illustrate each dimension with real-world examples that reveal how generative AI can produce or amplify misinformation, perpetuate representational harm, and create epistemic inequities, particularly in multilingual contexts. By highlighting these injustices, we aim to inform the development of epistemically just generative AI systems, proposing strategies for resistance, system design principles, and two approaches that leverage generative AI to foster a more equitable information ecosystem, thereby safeguarding democratic values and the integrity of knowledge production.

This paper introduces the "CIVICS: Culturally-Informed \& Values-Inclusive Corpus for Societal impacts" dataset, designed to evaluate the social and cultural variation of Large Language Models (LLMs) towards socially sensitive topics across multiple languages and cultures. The hand-crafted, multilingual dataset of statements addresses value-laden topics, including LGBTQI rights, social welfare, immigration, disability rights, and surrogacy. CIVICS is designed to elicit responses from LLMs to shed light on how values encoded in their parameters shape their behaviors. Through our dynamic annotation processes, tailored prompt design, and experiments, we investigate how open-weight LLMs respond to these issues, exploring their behavior across diverse linguistic and cultural contexts. Using two experimental set-ups based on log-probabilities and long-form responses, we show social and cultural variability across different LLMs. Specifically, different topics and sources lead to more pronounced differences across model answers, particularly on immigration, LGBTQI rights, and social welfare. Experiments on generating long-form responses from models tuned for user chat demonstrate that refusals are triggered disparately across different models, but consistently and more frequently in English or translated statements. As shown by our initial experimentation, the CIVICS dataset can serve as a tool for future research, promoting reproducibility and transparency across broader linguistic settings, and furthering the development of AI technologies that respect and reflect global cultural diversities and value pluralism. The CIVICS dataset and tools are made available under open licenses at hf.co/CIVICS-dataset.

Traditional recommender systems typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, text, images, and videos, enabling novel recommendation tasks. This comprehensive, multidisciplinary survey connects key advancements in RS using Generative Models (Gen-RecSys), covering: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges. This survey accompanies a "tutorial" presented at ACM KDD'24, with supporting materials provided at: https://encr.pw/vDhLq.

This paper investigates the direct risks and harms associated with modern text-to-image generative models, such as DALL-E and Midjourney, through a comprehensive literature review. While these models offer unprecedented capabilities for generating images, their development and use introduce new types of risk that require careful consideration. Our review reveals significant knowledge gaps concerning the understanding and treatment of these risks despite some already being addressed. We offer a taxonomy of risks across six key stakeholder groups, inclusive of unexplored issues, and suggest future research directions. We identify 22 distinct risk types, spanning issues from data bias to malicious use. The investigation presented here is intended to enhance the ongoing discourse on responsible model development and deployment. By highlighting previously overlooked risks and gaps, it aims to shape subsequent research and governance initiatives, guiding them toward the responsible, secure, and ethically conscious evolution of text-to-image models.

In this paper, we introduce new formal methods and provide empirical evidence to highlight a unique safety concern prevalent in reinforcement learning (RL)-based recommendation algorithms – ’user tampering.’ User tampering is a situation where an RL-based recommender system may manipulate a media user’s opinions through its suggestions as part of a policy to maximize long-term user engagement. We use formal techniques from causal modeling to critically analyze prevailing solutions proposed in the literature for implementing scalable RL-based recommendation systems, and we observe that these methods do not adequately prevent user tampering. Moreover, we evaluate existing mitigation strategies for reward tampering issues, and show that these methods are insufficient in addressing the distinct phenomenon of user tampering within the context of recommendations. We further reinforce our findings with a simulation study of an RL-based recommendation system focused on the dissemination of political content. Our study shows that a Q-learning algorithm consistently learns to exploit its opportunities to polarize simulated users with its early recommendations in order to have more consistent success with subsequent recommendations that align with this induced polarization. Our findings emphasize the necessity for developing safer RL-based recommendation systems and suggest that achieving such safety would require a fundamental shift in the design away from the approaches we have seen in the recent literature.

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research.

In this paper, we critically examine the effectiveness of the requirement to conduct a Data Protection Impact Assessment (DPIA) in Article 35 of the General Data Protection Regulation (GDPR) in light of fairness metrics. Through this analysis, we explore the role of the fairness principle as introduced in Article 5(1)(a) and its multifaceted interpretation in the obligation to conduct a DPIA. Our paper argues that although there is a significant theoretical role for the considerations of fairness in the DPIA process, an analysis of the various guidance documents issued by data protection authorities on the obligation to conduct a DPIA reveals that they rarely mention the fairness principle in practice. Our analysis questions this omission, and assesses the capacity of fairness metrics to be truly operationalized within DPIAs. We conclude by exploring the practical effectiveness of DPIA with particular reference to (1) technical challenges that have an impact on the usefulness of DPIAs irrespective of a controller's willingness to actively engage in the process, (2) the context dependent nature of the fairness principle, and (3) the key role played by data controllers in the determination of what is fair.

Computers are used to make decisions in an increasing number of domains. There is widespread agreement that some of these uses are ethically problematic. Far less clear is where ethical problems arise, and what might be done about them. This paper expands and defends the Ethical Gravity Thesis: ethical problems that arise at higher levels of analysis of an automated decision-making system are inherited by lower levels of analysis. Particular instantiations of systems can add new problems, but not ameliorate more general ones. We defend this thesis by adapting Marr's famous 1982 framework for understanding information-processing systems. We show how this framework allows one to situate ethical problems at the appropriate level of abstraction, which in turn can be used to target appropriate interventions.